2.1 Open New Project

When you first open RStudio you should see something like Figure 6.

Figure 6: What RStudio looks like when we first open it.

The first thing you want to do is open a new project.

A project is like a separate workspace you create for each of your areas of work, such as your thesis, or an assignment, or a project at work. Each project will have its own working director, files, folders, and history.

Step 1: To open a project go to File and then New Project, as shown in Figure 7.

Figure 7: Step 1: How to open a new project in RStudio.

Step 2: In the window that opens, you have a number of choices. At this stage, select “New Director”, as shown in Figure 8.

Figure 8: Step 2: How to open a new project in RStudio.

Step 3: Select “New Project”, as shown in Figure 9.

Figure 9: Step 3: How to open a new project in RStudio.

Step 4: Type in the name of your new directory. In my case I am creating one called “2019, SOCI832 Practice” Figure 10.

Figure 10: Step 4: How to open a new project in RStudio.

Step 5: RStudio will now open a new project for you. You can see the name of the folder at the top of the RStudio window, and also under the ‘Console’ tab.

2.2 Open Script file

The next thing we want to do, before we really get started with RStudio, is open a script file.

As you probably know, we need to type commands into R - we can’t just point and click like we do in Microsoft Word or SPSS.

When we type commands, two things happen:

• We make mistakes - typos.
• We often want to run our commands again - repeating the same or similar analysis.
• We often want to save our commands so we know what we did.

Using a script - which is basically just a text file with all our commands in it - allows us to correct our mistakes, save our commands, and also copy and paste them later when we want to do similar analysis.

Step 1: To open a script file go to File menu and then New File and then R Script as shown in Figure 11.

Figure 11: Step 4: How to open a new script file in RStudio.

There are also two short cuts to doing this:

• On the keyboard (Windows), you can type: Ctrl+Shift+N
• You can also click the white paper icon with the green plus sign on it in the top left corner of the RStudio screen.

Step 2: To save your script file go to the menu File and then Save As. Navigate to your folder (in my case C:019, SOCI832 Practice), and then give your script file a name. I will call mine nicks_script. And press Save

2.3 Running your first script

Let’s run your first script.

In the script window you can type the classic first command of any book that teaches programming.

print("Hello World")

And then put your cursor anywhere on that line of code in the script window and press Ctrl+Enter (in Windows) or Cmd+Enter (on a Mac).

In the console window (below the script) you should see this.

## [1] "Hello World"

Actually, what you will see in the console window is what appears in Figure 12.

Figure 12: Output in Console window from print("Hello World") command.

What does it mean?

It is a command that says “Print to the console the text inside the double inverted commas”, which in our case is “Hello World”.

It is a pretty useless command, but it shows how you can run a line in a script file, and see the results in the console window.

2.4 Looking around R Studio

Lets take a quick look around RStudio, and orient ourselves.

Figure 13 shows what RStudio looks like in a default setting.

Figure 13: Looking around the RStudio interface.

The numbers in Figure 13 refer to:

1. Script Window: Where you type and save your commands as a text file.
2. Console Window: Where the output of our R commands appears. The commands we run will appear in blue, while the output will appear in black.
3. Environment Tab: This is where all your currently open data will appear. At the moment you can see that I have created a variable called “x”, which holds the value “Hello World Again”.
4. History Tab: This contains the history of all the commands we have recently run in this project. We can go to this tab and re-run any commands we have run previously.
5. Files Tab: This shows the files and folders within our project folder.
   
6. Plots Tab: Where graphs and figures will appear, when we make them.
7. Packages Tab: A tab that makes it easier to install Packages in R.
8. Help Tab: The Help window
9. Viewer Tab: A built in browser in RStudio, which can be used to open webpages (e.g. HTML files) created by R.
10. Save Button: Click to save your current script
11. Find Button: To find text in your script
12. Run Button: To run any highlighted script.

3.1 Simple objects

We can create a simple object with this command:

x <- "Hello World"

In R we call <- the assignment operator. It says “Put the value on the right into the object name on the left.”

You can see that x appears in the Environment tab in the top right screen.

We can see the object x by simply typing x and then running that line of code

x

## [1] "Hello World"

We can also do this with a number, for example:

y <- 9

y appears in the environment window.

And now we can look at the contents of y by just typing y and running the line of code:

y

## [1] 9

Notice if we assign some different value to y, then y changes:

y <- 9
y

## [1] 9

y <- 2
y

## [1] 2

We can also delete variables with the command rm(). Notice when we call y after we have removed it, we get an error.

y <- 9
y

## [1] 9

rm(y)
y

## Error in eval(expr, envir, enclos): object 'y' not found

3.2 Calculations in R

We can do calculations in R by entering them as you would normally, as in a calculator.

For instance, you could calculate 1+1 by typing in and running the following piece of code.

1+1

## [1] 2

We can also do calculations on variables. In the next example we create new variables, and then call them to see their contents.

a <- 100
a
b <- 3
b
c <- a + b
c
d <- a * b
d
e <- a / b
e
f <- a - b
f

## [1] 100

## [1] 3

## [1] 103

## [1] 300

## [1] 33.33333

## [1] 97

3.3 An introduction to functions

Most of the power of R is contained in commands that are called ‘functions’.

Functions take one or more variables, process these variables, and produce an output.

The conventional syntax for naming most functions in R is

1. The name of the function
2. A round open bracket “(”
3. One or more arguments: variables or values being passed to the function
4. A round close bracket “)”   Example 1: For example, we might create a variable called z and put the value 100 into it. And then we run the function “sqrt()” on it. “sqrt()” computers the square root of the number in the brackets.

z <- 100
sqrt(z)

## [1] 10

We can see that R returns the square root of 100, which is 10.

Example 2: We can do a more complex example with a vector. We can create a vector with the function “c()”, and then we can calculate the sum with “sum()”, the mean with “mean()”, and the standard deviation with the function “sd()”.

q <- c(1,2,3,4,5,6,7,8,9,10)
sum(q)
mean(q)
sd(q)

## [1] 55

## [1] 5.5

## [1] 3.02765

5.1. Saving R data frames and other objects

We have created a data frame with the rabbit data in it. We could save that to our computer if we wanted. This will save it into a file in our working directory.

There are three main formats to save standard dataframe in:

• CSV: comma separated values, which are basically a very simple form of spreadsheet
• rds: saves a single R object. Has the advantage that you can load the object into any name when you restore it.
• RData: saves a single or multiple objects. Has the disadvantage that it can only load objects back into their original name when you restore it.

write.csv(rabbits, file = "rabbits.csv")
saveRDS(rabbits, file = "my_data.rds")
a <- c(1,2,3,4,5)
b <- c("one", "ten", "one hundred")
save(rabbits, a, b, file = "much_data.RData")

5.2 Reading data from files

We can then read this data back into R using any one of the three following commands.

flatmates <- read.csv(file="rabbits.csv", header=TRUE, sep=",")
flatmates_from_rds <- readRDS("my_data.rds")
load("much_data.RData")

5.3 Reading from Excel Files

If we want to read data from Excel files, we need to download it to our working directory, and install the package readxl, and then use the command read_excel.

install.packages("readxl", dependencies = TRUE)
library(readxl)
my_excel_file <- read_excel("excel-example.xlsx")

5.4 Reading from SPSS files

If we want to read data from SPSS files (called .sav files) then we need

install.packages("sjlabelled", dependencies = TRUE)
library(sjlabelled) 
my_spss_file <- read_spss("spss-example.sav")
my_stata_file <- read_stata("stata-example.dta")

5.5 Reading from standard datasets in R

data()
data(ChickWeight)
str(ChickWeight)
?ChickWeight

5.6 Importing from a website

library(sjlabelled) 
guardian_from_csv <- read.csv(url("https://methods101.com/data/guard_data.csv"), header=TRUE, sep=",")
guardian_from_rds <- readRDS(url("https://methods101.com/data/guard_data.rds"))
guardian_from_spss <- sjlabelled::read_spss(url("https://methods101.com/data/guard_data.sav"))

You can also open the data from an Excel file you can download from https://www.methods101.com by:

1. going to https://methods101.com/data/guard_data.xlsx and
2. downloading the .xlsx file to your computer, and then
3. saving the file to your working directory and
4. making sure you have readxl package installed and loaded with the command library(readxl), and then
5. opening it in RStudio with the command guardian_from_excel <- readxl::read_xlsx("url("https://methods101.com/data/"guard_data.sav"))